Remove model_name, use model.

docs/config_guide_cn.md

@@ -37,16 +37,16 @@ source = [HuggingFace, OpenAI, DashScope]
 
 目前, pai_rag 支持以上三种 embedding 源.
 
-如果 source = "HuggingFace", 您需要进一步指定 model_name 和 embed_batch_size。默认的模型名称和批处理大小分别为 bge-large-zh-v1.5 和 10。
+如果 source = "HuggingFace", 您需要进一步指定 model 和 embed_batch_size。默认的模型名称和批处理大小分别为 bge-large-zh-v1.5 和 10。
 
     source = "HuggingFace"
-    model_name = "bge-large-zh-v1.5"
+    model = "bge-large-zh-v1.5"
     embed_batch_size = 10
 
 或者, 如果你想使用其它 huggingface 模型, 请指定如下参数：
 
     source = "HuggingFace"
-    model_name = "xxx"
+    model = "xxx"
     model_dir = "xxx/xxx"
     embed_batch_size = 20 (for example)
 

docs/config_guide_en.md

@@ -37,16 +37,16 @@ source = [HuggingFace, OpenAI, DashScope]
 
 Currently, pai_rag supports three embedding sources.
 
-If source = "HuggingFace", you need to further specify model_name and embed_batch_size. The default model name and batch size are bge-large-zh-v1.5 and 10, respectively.
+If source = "HuggingFace", you need to further specify model_modelname and embed_batch_size. The default model name and batch size are bge-large-zh-v1.5 and 10, respectively.
 
     source = "HuggingFace"
-    model_name = "bge-large-zh-v1.5"
+    model = "bge-large-zh-v1.5"
     embed_batch_size = 10
 
 Alternatively, if you want to use other huggingface models, please specify parameters as below:
 
     source = "HuggingFace"
-    model_name = "xxx"
+    model = "xxx"
     model_dir = "xxx/xxx"
     embed_batch_size = 20 (for example)
 

example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml

@@ -26,10 +26,10 @@ persist_path = "localdata/storage"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -49,11 +49,11 @@ vector_store.type = "FAISS"
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = "DashScope"
-name = "qwen2-7b-instruct"
+model = "qwen2-7b-instruct"
 
 [rag.llm.multi_modal]
 source = ""

example_data/function_tools/python-tool-for-booking-demo/settings.toml

@@ -26,10 +26,10 @@ persist_path = "localdata/storage"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -49,11 +49,11 @@ vector_store.type = "FAISS"
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = "DashScope"
-name = "qwen2-7b-instruct"
+model = "qwen2-7b-instruct"
 
 [rag.llm.multi_modal]
 source = ""

src/pai_rag/app/web/rag_client.py

@@ -415,7 +415,6 @@ def patch_config(self, update_dict: Any):
         view_model: ViewModel = ViewModel.from_app_config(config)
         view_model.update(update_dict)
         new_config = view_model.to_app_config()
-
         r = requests.patch(
             self.config_url, json=new_config, timeout=DEFAULT_CLIENT_TIME_OUT
         )

src/pai_rag/app/web/view_model.py

@@ -47,7 +47,7 @@ class ViewModel(BaseModel):
     llm: str = "PaiEas"
     llm_eas_url: str = None
     llm_eas_token: str = None
-    llm_eas_model_name: str = "model_name"
+    llm_eas_model_name: str = "model"
     llm_api_key: str = None
     llm_api_model_name: str = None
     llm_temperature: float = 0.1
@@ -57,7 +57,7 @@ class ViewModel(BaseModel):
     mllm: str = None
     mllm_eas_url: str = None
     mllm_eas_token: str = None
-    mllm_eas_model_name: str = "model_name"
+    mllm_eas_model_name: str = "model"
     mllm_api_key: str = None
     mllm_api_model_name: str = None
 
@@ -189,7 +189,7 @@ def from_app_config(config):
             "source", view_model.embed_source
         )
         view_model.embed_model = config["embedding"].get(
-            "model_name", view_model.embed_model
+            "model", view_model.embed_model
         )
         view_model.embed_api_key = config["embedding"].get(
             "api_key", view_model.embed_api_key
@@ -207,11 +207,11 @@ def from_app_config(config):
         )
         if view_model.llm.lower() == "paieas":
             view_model.llm_eas_model_name = config["llm"].get(
-                "model_name", view_model.llm_eas_model_name
+                "model", view_model.llm_eas_model_name
             )
         else:
             view_model.llm_api_model_name = config["llm"].get(
-                "model_name", view_model.llm_api_model_name
+                "model", view_model.llm_api_model_name
             )
 
         view_model.use_mllm = config["llm"]["multi_modal"].get(
@@ -233,14 +233,14 @@ def from_app_config(config):
                 "view_model.mllm_eas_model_name",
                 view_model.mllm_eas_model_name,
                 "2",
-                config["llm"]["multi_modal"]["model_name"],
+                config["llm"]["multi_modal"]["model"],
             )
             view_model.mllm_eas_model_name = config["llm"]["multi_modal"].get(
-                "model_name", view_model.mllm_eas_model_name
+                "model", view_model.mllm_eas_model_name
             )
         else:
             view_model.mllm_api_model_name = config["llm"]["multi_modal"].get(
-                "model_name", view_model.mllm_api_model_name
+                "model", view_model.mllm_api_model_name
             )
 
         view_model.use_oss = config["oss_store"].get("enable", view_model.use_oss)
@@ -436,7 +436,7 @@ def to_app_config(self):
         config = recursive_dict()
 
         config["embedding"]["source"] = self.embed_source
-        config["embedding"]["model_name"] = self.embed_model
+        config["embedding"]["model"] = self.embed_model
         config["embedding"]["api_key"] = self.embed_api_key
         config["embedding"]["embed_batch_size"] = int(self.embed_batch_size)
 
@@ -446,19 +446,19 @@ def to_app_config(self):
         config["llm"]["api_key"] = self.llm_api_key
         config["llm"]["temperature"] = self.llm_temperature
         if self.llm.lower() == "paieas":
-            config["llm"]["model_name"] = self.llm_eas_model_name
+            config["llm"]["model"] = self.llm_eas_model_name
         else:
-            config["llm"]["model_name"] = self.llm_api_model_name
+            config["llm"]["model"] = self.llm_api_model_name
 
         config["llm"]["multi_modal"]["enable"] = self.use_mllm
         config["llm"]["multi_modal"]["source"] = self.mllm
         config["llm"]["multi_modal"]["endpoint"] = self.mllm_eas_url
         config["llm"]["multi_modal"]["token"] = self.mllm_eas_token
         config["llm"]["multi_modal"]["api_key"] = self.mllm_api_key
         if self.mllm.lower() == "paieas":
-            config["llm"]["multi_modal"]["model_name"] = self.mllm_eas_model_name
+            config["llm"]["multi_modal"]["model"] = self.mllm_eas_model_name
         else:
-            config["llm"]["multi_modal"]["model_name"] = self.mllm_api_model_name
+            config["llm"]["multi_modal"]["model"] = self.mllm_api_model_name
 
         config["oss_store"]["enable"] = self.use_oss
         if os.getenv("OSS_ACCESS_KEY_ID") is None and self.oss_ak:
@@ -728,7 +728,7 @@ def to_component_settings(self) -> Dict[str, Dict[str, Any]]:
             "visible": self.llm.lower() == "paieas",
         }
         if self.llm.lower() == "paieas" and not self.llm_eas_model_name:
-            self.llm_eas_model_name = "model_name"
+            self.llm_eas_model_name = "model"
 
         settings["llm_eas_model_name"] = {
             "value": self.llm_eas_model_name,

src/pai_rag/config/settings.toml

@@ -33,10 +33,10 @@ type = "local"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -53,12 +53,12 @@ vector_store.type = "FAISS"
 # llm configurations, source support API: OpenAI,DashScope or PAI-EAS's deployment
 # eg.
 # source = "PaiEas"
-# name = ""
+# model = ""
 # endpoint = ""
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = ""

src/pai_rag/evaluation/settings_eval.toml

@@ -33,10 +33,10 @@ type = "local"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -53,20 +53,20 @@ vector_store.type = "FAISS"
 # llm configurations, source support API: OpenAI,DashScope or PAI-EAS's deployment
 # eg.
 # source = "PaiEas"
-# name = ""
+# model = ""
 # endpoint = ""
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = ""
 
 [rag.llm.multi_modal]
 enable = true
 source = "DashScope"
-name = "qwen-vl-plus"
+model = "qwen-vl-plus"
 
 [rag.node_enhancement]
 tree_depth = 3

src/pai_rag/integrations/embeddings/pai/embedding_utils.py

@@ -36,22 +36,22 @@ def create_embedding(embed_config: PaiBaseEmbeddingConfig):
     elif isinstance(embed_config, HuggingFaceEmbeddingConfig):
         pai_model_dir = os.getenv("PAI_RAG_MODEL_DIR", "./model_repository")
         embed_model = HuggingFaceEmbedding(
-            model_name=os.path.join(pai_model_dir, embed_config.model_name),
+            model_name=os.path.join(pai_model_dir, embed_config.model),
             embed_batch_size=embed_config.embed_batch_size,
             trust_remote_code=True,
         )
 
         logger.info(
-            f"Initialized HuggingFace embedding model {embed_config.model_name} from model_dir_path {pai_model_dir} with {embed_config.embed_batch_size} batch size."
+            f"Initialized HuggingFace embedding model {embed_config.model} from model_dir_path {pai_model_dir} with {embed_config.embed_batch_size} batch size."
         )
 
     elif isinstance(embed_config, CnClipEmbeddingConfig):
         embed_model = CnClipEmbedding(
-            model_name=embed_config.model_name,
+            model_name=embed_config.model,
             embed_batch_size=embed_config.embed_batch_size,
         )
         logger.info(
-            f"Initialized CnClip embedding model {embed_config.model_name} with {embed_config.embed_batch_size} batch size."
+            f"Initialized CnClip embedding model {embed_config.model} with {embed_config.embed_batch_size} batch size."
         )
 
     else:

src/pai_rag/integrations/embeddings/pai/pai_embedding_config.py

@@ -13,7 +13,7 @@ class SupportedEmbedType(str, Enum):
 
 class PaiBaseEmbeddingConfig(BaseModel):
     source: SupportedEmbedType
-    model_name: str
+    model: str
     embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE
 
     class Config:
@@ -36,24 +36,24 @@ def validate_case_insensitive(cls, value):
 
 class DashScopeEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.dashscope] = SupportedEmbedType.dashscope
-    model_name: str | None = None  # use default
+    model: str | None = None  # use default
     api_key: str | None = None  # use default
 
 
 class OpenAIEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.openai] = SupportedEmbedType.openai
-    model_name: str | None = None  # use default
+    model: str | None = None  # use default
     api_key: str | None = None  # use default
 
 
 class HuggingFaceEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.huggingface] = SupportedEmbedType.huggingface
-    model_name: str | None = "bge-large-zh-v1.5"
+    model: str | None = "bge-large-zh-v1.5"
 
 
 class CnClipEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.cnclip] = SupportedEmbedType.cnclip
-    model_name: str | None = "ViT-L-14"
+    model: str | None = "ViT-L-14"
 
 
 SupporttedEmbeddingClsMap = {
@@ -73,6 +73,6 @@ def parse_embed_config(config_data):
 
 
 if __name__ == "__main__":
-    embedding_config_data = {"source": "Openai", "model_name": "gpt-1", "api_key": None}
+    embedding_config_data = {"source": "Openai", "model": "gpt-1", "api_key": None}
 
     print(parse_embed_config(embedding_config_data))

src/pai_rag/integrations/llms/pai/llm_config.py

@@ -126,7 +126,7 @@ class PaiBaseLlmConfig(BaseModel):
     temperature: float = DEFAULT_TEMPERATURE
     system_prompt: str = None
     max_tokens: int = DEFAULT_MAX_TOKENS
-    model_name: str = None
+    model: str = None
 
     @classmethod
     def get_subclasses(cls):
@@ -150,20 +150,20 @@ class DashScopeLlmConfig(PaiBaseLlmConfig):
     source: Literal[SupportedLlmType.dashscope] = SupportedLlmType.dashscope
     api_key: str | None = None
     base_url: str = "https://dashscope.aliyuncs.com/compatible-mode/v1"
-    model_name: str = "qwen-turbo"
+    model: str = "qwen-turbo"
 
 
 class OpenAILlmConfig(PaiBaseLlmConfig):
     source: Literal[SupportedLlmType.openai] = SupportedLlmType.openai
     api_key: str | None = None
-    model_name: str = "gpt-3.5-turbo"
+    model: str = "gpt-3.5-turbo"
 
 
 class PaiEasLlmConfig(PaiBaseLlmConfig):
     source: Literal[SupportedLlmType.paieas] = SupportedLlmType.paieas
     endpoint: str
     token: str
-    model_name: str = "default"
+    model: str = "default"
 
 
 SupporttedLlmClsMap = {cls.get_type(): cls for cls in PaiBaseLlmConfig.get_subclasses()}
@@ -183,7 +183,7 @@ def parse_llm_config(config_data):
 if __name__ == "__main__":
     llm_config_data = {
         "source": "dashscope",
-        "model_name": "qwen-turbo",
+        "model": "qwen-turbo",
         "api_key": None,
         "max_tokens": 1024,
     }